大中专在校学生考试分数的正态化线性转换

本文分析了考试记分方法中传统的绝对百分体制的缺陷，给出了一种正态化转换分数的算法，较适合于在校大中专学生学习水平的横向评价。     

基本剩余价值初探

剩余价值在其历史发展中相继地采取了绝对剩余价值、相对剩余价值和基本剩余价值的直接表现形式。基本剩余价值是在绝对剩余价值和相对剩余价值尚未产生或产而复失的历史场合中必须生产的 ,也是在实行绝对剩余价值生产和相对剩余价值生产的历史场合中 ,被它们凸现出来并使它们赖以产生的具有原先既定性 ,或扩张基础性和增生本体性的剩余价值形式。基本剩余价值生产与资本主义生产相始终 ,是资本主义生产目的的基本实现。它和绝对剩余价值、相对剩余价值在质和量及其历史形态上的差异 ,使剩余价值成为一个比较经济范畴     

Residual analysis for spatial point processes (with discussion)

Summary.  We define residuals for point process models fitted to spatial point pattern data, and we propose diagnostic plots based on them. The residuals apply to any point process model that has a conditional intensity; the model may exhibit spatial heterogeneity, interpoint interaction and dependence on spatial covariates. Some existing ad hoc methods for model checking (quadrat counts, scan statistic, kernel smoothed intensity and Berman's diagnostic) are recovered as special cases. Diagnostic tools are developed systematically, by using an analogy between our spatial residuals and the usual residuals for (non-spatial) generalized linear models. The conditional intensity λ plays the role of the mean response. This makes it possible to adapt existing knowledge about model validation for generalized linear models to the spatial point process context, giving recommendations for diagnostic plots. A plot of smoothed residuals against spatial location, or against a spatial covariate, is effective in diagnosing spatial trend or co-variate effects. Q – Q -plots of the residuals are effective in diagnosing interpoint interaction.     

全球视野内的中国贫困问题研究

中国贫困问题的研究应顺应贫困研究国际趋势其中国贫困走向进行相应调整,即顺应贫困演变趋势,从研究绝对贫困向研究相对贫困转化;改变政策取向,从关注集体贫困向关注个体贫困转化;拓宽研究范围,从研究物质贫困向人文贫困转化;转变研究方法,从一般经济学分析拓宽到制度经济学分析。在对策设计上,应从加强反贫困制度建设、实行瞄准贫困人口的扶贫政策、缩小收入差距和关注人文贫困等方面加以考虑。     

没有场的空间是不存在的——广义相对论的空间概念

本文简述了空间概念的发展历程。经典力学和SR把空间看作虚无,而GR则认为空间是 物质的。从而指出了空间概念是人类思维的自由创造。     

On Schelling＇ s View of Art and Nature

艺术与自然的关系,一直是西方古典美学中一个争论不休的问题。从古希腊到近代德国的美学家们都曾加以思考,并提出了不同的看法。作为德国古典美学代表人物之一的谢林,对艺术与自然的看法与他对艺术本质的理解是一致的。他反驳了两种流行的观点,并提出自己的独特理念。谢林的看法,与康德和黑格尔有所不同。谢林的论述,对我们今天认识艺术与自然、人与自然之关系仍有借鉴意义。     

Alternative tests for heteroscedasticity of disturbances:a comparative study

This paper presents three small sample tests for testing the heteroscedasticity among regression disturbances. The power of these tests are compared with two of the leading tests for this hypothesis, one by Goldfeld and Quandt [5] and the other by Theil [17]. We also provide a heuristic method of selecting the number of middle observations to be deleted for the Goldfeld-Quandt type of tests.     

Bootstrapping regression models with BLUS residuals

To bootstrap a regression problem, pairs of response and explanatory variables or residuals can be resam‐pled, according to whether we believe that the explanatory variables are random or fixed. In the latter case, different residuals have been proposed in the literature, including the ordinary residuals (Efron 1979), standardized residuals (Bickel & Freedman 1983) and Studentized residuals (Weber 1984). Freedman (1981) has shown that the bootstrap from ordinary residuals is asymptotically valid when the number of cases increases and the number of variables is fixed. Bickel & Freedman (1983) have shown the asymptotic validity for ordinary residuals when the number of variables and the number of cases both increase, provided that the ratio of the two converges to zero at an appropriate rate. In this paper, the authors introduce the use of BLUS (Best Linear Unbiased with Scalar covariance matrix) residuals in bootstrapping regression models. The main advantage of the BLUS residuals, introduced in Theil (1965), is that they are uncorrelated. The main disadvantage is that only n —p residuals can be computed for a regression problem with n cases and p variables. The asymptotic results of Freedman (1981) and Bickel & Freedman (1983) for the ordinary (and standardized) residuals are generalized to the BLUS residuals. A small simulation study shows that even though only n — p residuals are available, in small samples bootstrapping BLUS residuals can be as good as, and sometimes better than, bootstrapping from standardized or Studentized residuals.     

A Weighted Least Squares Approach to Levene's Test of Homogeneity of Variance

In 1960 Levene suggested a potentially robust test of homogeneity of variance based on an ordinary least squares analysis of variance of the absolute values of mean-based residuals. Levene's test has since been shown to have inflated levels of significance when based on the F-distribution, and tests a hypothesis other than homogeneity of variance when treatments are unequally replicated, but the incorrect formulation is now standard output in several statistical packages. This paper develops a weighted least squares analysis of variance of the absolute values of both mean-based and median-based residuals. It shows how to adjust the residuals so that tests using the F -statistic focus on homogeneity of variance for both balanced and unbalanced designs. It shows how to modify the F -statistics currently produced by statistical packages so that the distribution of the resultant test statistic is closer to an F-distribution than is currently the case. The weighted least squares approach also produces component mean squares that are unbiased irrespective of which variable is used in Levene's test. To complete this aspect of the investigation the paper derives exact second-order moments of the component sums of squares used in the calculation of the mean-based test statistic. It shows that, for large samples, both ordinary and weighted least squares test statistics are equivalent; however they are over-dispersed compared to an F variable.     

Diagnostics of multiple group influential observations for logistic regression models

In this paper, two new multiple influential observation detection methods, GCD.GSPR and mCD*, are introduced for logistic regression. The proposed diagnostic measures are compared with the generalized difference in fits (GDFFITS) and the generalized squared difference in beta (GSDFBETA), which are multiple influential diagnostics. The simulation study is conducted with one, two and five independent variable logistic regression models. The performance of the diagnostic measures is examined for a single contaminated independent variable for each model and in the case where all the independent variables are contaminated with certain contamination rates and intensity. In addition, the performance of the diagnostic measures is compared in terms of the correct identification rate and swamping rate via a frequently referred to data set in the literature.